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Synergetic structure-control design via a hybrid gradient-evolutionary algorithm

optimization AND ENGINEERING 2015年第3期16卷 511-539页

作者： Villarreal-Cervantes, Miguel G. Alberto Cruz-Villar, Carlos Alvarez-Gallegos, Jaime Inst Politecn Nacl CIDETEC Mechatron Sect Mexico City DF Mexico CINVESTAV Dept Elect Engn Mechatron Sect Mexico City 14000 DF Mexico

This paper proposes a synergetic approach to design a planar parallel robot with its control system. In this proposal, the design problem is stated as a dynamic optimization problem with dynamic and static constraints on both the robot parameters and the control input to the robot. Control parameterization via PID controllers is used to rewrite the dynamic optimization problem as a nonlinear programming problem, which is solved by using a hybrid gradient-evolutionary optimization technique. The dynamic optimization problem presents singularity regions in the design space requiring the use of the proposed hybrid gradient-evolutionary optimization technique. The rationale behind the proposed hybrid algorithm lies in using a exploratory search mechanism for finding the initial guess to the fine search mechanism, which is used to search in a local region of a solution. We discuss both the results of the proposed optimization technique and the experimental results of the robot designed with the proposed approach. In addition, the result provided by the proposed synergetic design approach is compared with a sequential design approach, showing the advantages of the synergetic approach.

关键词： hybrid optimization algorithm Structure-control design Parallel robot Differential evolution

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Predictive Control Strategy Based on Extreme Learning Machine for Path-Tracking of Autonomous Mobile Robot

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INTELLIGENT AUTOMATION AND SOFT COMPUTING 2015年第1期21卷 1-19页

作者： Yang, Yimin Lin, Xiaofeng Miao, Zhiqiang Yuan, Xiaofang Wang, Yaonan Guangxi Univ Coll Elect Engn Nanning 530004 Peoples R China Hunan Univ Coll Elect & Informat Engn Changsha 410082 Hunan Peoples R China

In this paper, we propose a novel nonlinear predictive control strategy based on an extreme learning machine to address the path-tracking control problem of wheeled mobile robots in the presence external disturbances. The hybrid chaotic optimization algorithm (HCOA), which can avoid being trapped in local minima and improve convergence in dealing with the large space and high-dimension optimization problems, is used to perform real-time nonlinear minimization of the cost function of a mobile robot to enhance the control accuracy. The proposed improved bidirectional extreme learning machine is employed to model the mobile robot plant and estimate future plant output. The experimental results of tracking the automation mobile robot indicate that the proposed controller can provide more accuracy and faster tracking performance than traditional neural network predictive controllers.

关键词： Bidirectional Extreme Learning Machine Model Predictive Control hybrid optimization algorithm Neural Network

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Improved performances of a PMSM with reduced torque ripples, optimized based on hybrid algorithm, dedicated for light EV 12

Improved performances of a PMSM with reduced torque ripples,...

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12th IEEE Vehicle Power and Propulsion Conference (VPPC)

作者： Fodorean, Daniel Idoumghar, Lhassane Tech Univ Cluj Napoca Cluj Napoca Romania Univ Haute Alsace Mulhouse France

ISBN: (纸本)9781467376372

This paper deals with the optimum design of a permanent magnet synchronous machine (PMSM) used for the propulsion of a light electric vehicle. The optimization is employed based on evolutionary algorithm by combining the simulated annealing with the particle swarm approach. The optimized solution is validated numerically through finite element method (FEM). Furthermore, a prototype of the optimized machine is constructed and the control of the motor operation is tested, showing that the obtained results are validating the employed approach and the prototype corresponds to the application needs.

关键词： light electric vehicle PMSM hybrid optimization algorithm FEM validation measurements

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The application of hybrid optimization algorithm in joint inversion of surface and borehole magnetic data

The application of hybrid optimization algorithm in joint in...

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The 4th International Conference on Environmental and Engineering Geophysics

作者： Dalian Zhang,Tianyou Liu Institute of Geophysics and Geomatics,China University of Geosciences,Wuhan,430074,China

Global optimization algorithm has strong generality without the use of problem's specific information,which resulted in a waste of information on known *** optimization algorithms has strong dependence of the problem,but the utilization of information on specific problems can be quickly constructed a solution,its time performance is more *** optimization algorithm combines the advantages of the local optimization algorithm and global optimization algorithm and overcomes their *** can not only increase the computing speed,but also have a very good effect on improving the quality of *** Swarm optimization(PSO) is a new efficient and parallel optimization algorithm,this algorithm has a profound intelligence background,and is simple and easy to *** paper gives out the process of the hybrid optimization inversion which combined PSO algorithm with the singular value truncated method,and creates a vertical cuboid as the model,then uses this hybrid optimization method for the joint inversion of surface and borehole magnetic data in the theoretical model tests and the practical example of Daye exhausted *** result shows that the relative error of this hybrid optimization algorithm is smaller than the above two kinds of algorithms,and the computing time is between the two *** order to find the balance of time-consuming and precision,using hybrid algorithm is superior than using the two single algorithm.

关键词： hybrid optimization algorithm PSO Singular Value Truncated Method Joint Inversion

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KP Model for Hysteresis of Piezoelectric Ceramic Actuators

KP Model for Hysteresis of Piezoelectric Ceramic Actuators

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Chinese Automation Congress (CAC)

作者： Liu, Ying Zhou, Miaolei Jilin Univ Dept Control Sci & Engn Changchun Peoples R China

ISBN: (纸本)9781467371896

The hysteresis characteristic is exhibited in piezoelectric ceramic actuators. For the purpose of describing the hysteresis characteristic between input voltage and output displacement of piezoelectric ceramic actuators, a modeling method based on Krasnosel'skii-Pokrovskii model is adopted in this paper. The density function of the Krasnosel'skii-Pokrovskii model is identified by cuckoo search algorithm and hybrid optimization algorithm of particle swarm and cuckoo search. The simulation results show that the maximum displacement error is 0.29 mu m when the density function is identified by cuckoo search algorithm and the maximum displacement error is 0.22 mu m when the density function is identified by hybrid optimization algorithm of particle swarm and cuckoo search. By comparing two identification methods above, it can be seen that the Krasnosel'skii-Pokrovskii model identified by hybrid optimization algorithm can accurately describe the hysteresis characteristic of piezoelectric ceramic actuators.

关键词： hysteresis piezoelectric ceramic actuators Krasnosel'skii-Pokrovskii model cuckoo search algorithm hybrid optimization algorithm

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A Summary of Reactive Power optimization algorithms

A Summary of Reactive Power Optimization Algorithms

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2015 4th International Conference on Computer, Mechatronics, Control and Electronic Engineering (ICCMCEE 2015)

作者： Dongyue Zhang Kewen Wang School of Electrical Engineering Zhengzhou University

ISBN: (纸本)9781510813465

In order to comprehensively generalize the research status quo, achievements as well as deficiencies on the power system reactive power optimization algorithms, a universal reactive power optimization model was introduced in this paper, and a variety of common traditional optimization algorithms, artificial intelligence optimization algorithms used in current power system reactive power optimization were summarized, including linear programming, nonlinear programming, dynamic programming, modern heuristic search, expert system method, etc. The principle, advantages and disadvantages of each algorithm were analyzed in this paper, at the same time, several improved optimization algorithms which aimed at the flaws of each basic optimization algorithm were also introduced. Finally, the paper listed several effective hybrid optimization algorithms, and offered a proposal on the research direction of reactive power optimization in the future.

关键词： power system reactive power optimization traditional algorithms artificial intelligence optimization algorithm hybrid optimization algorithm

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A Novel Assembly Line Balancing optimization algorithm based on PSOSA

A Novel Assembly Line Balancing Optimization Algorithm based...

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2015 4th International Conference on Computer, Mechatronics, Control and Electronic Engineering (ICCMCEE 2015)

作者： Xiaomei Hu Yubin Wang Yangyang Zhang Xinran Shi The Key Laboratory of Intelligent Manufacturing and Robotics School of Mechatronic Engineering and Automation Shanghai University Department of Mechanical Engineering University of Michigan

ISBN: (纸本)9781510813465

Assembly line is widely used in manufacturing system and assembly line balancing problem directly affects the productivity of the whole manufacturing system. In the actual situation of assembly process, optimizing a certain goal in the process of actual assembly individually may cause the neglect of relevance of balance goals, so a dual-objective optimization model is proposed. Although there are many ways to solve the assembly line balancing problem, most of them have a slow convergence speed and poor optimize result. A PSOSA hybrid optimization algorithm is put forward to improve the solution quality. The Metropolis criterion of SA algorithm is integrated into PSO algorithm to overcome the precocious phenomenon of PSO and long optimization time of SA. So the PSOSA hybrid optimization algorithm has the advantages of PSO and SA algorithm. The simulation results show the feasibility and effectiveness of the assembly line balancing optimization algorithm based on PSOSA algorithm.

关键词： assembly line balancing optimization PSOSA hybrid optimization algorithm

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Velocity based artificial bee colony algorithm for high dimensional continuous optimization problems

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2014年第0期36卷 148-163页

作者： Imanian, Nafiseh Shiri, Mohammad Ebrahim Moradi, Parham Amirkabir Univ Technol Dept Math & Comp Sci Tehran Iran Univ Kurdistan Dept Comp Engn Sanandaj Iran

Artificial bee colony (ABC) is a swarm optimization algorithm which has been shown to be more effective than the other population based algorithms such as genetic algorithm (GA), particle swarm optimization (PSO) and ant colony optimization (ACO). Since it was invented, it has received significant interest from researchers studying in different fields because of having fewer control parameters, high global search ability and ease of implementation. Although ABC is good at exploration, the main drawback is its poor exploitation which results in an issue on convergence speed in some cases. Inspired by particle swarm optimization, we propose a modified ABC algorithm called VABC, to overcome this insufficiency by applying a new search equation in the onlooker phase, which uses the PSO search strategy to guide the search for candidate solutions. The experimental results tested on numerical benchmark functions show that the VABC has good performance compared with PSO and ABC. Moreover, the performance of the proposed algorithm is also compared with those of state-of-the-art hybrid methods and the results demonstrate that the proposed method has a higher convergence speed and better search ability for almost all functions. (C) 2014 Elsevier Ltd. All rights reserved.

关键词： Particle swarm optimization Artificial bee colony Continuous optimization hybrid optimization algorithm

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Improved performances of a PMSM with reduced torque ripples, optimized based on hybrid algorithm, dedicated for light EV

Improved performances of a PMSM with reduced torque ripples,...

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IEEE Vehicle Power and Propulsion Conference

作者： Daniel FODOREAN Lhassane IDOUMGHAR Technical University of Cluj-Napoca Universite de Haute-Alsace

ISBN: (纸本)9781467376389

关键词： Light electric vehicle PMSM hybrid optimization algorithm FEM validation Measurements

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Multi-state Markov process Based Maintenance optimization for Power Distribution Transformer

Multi-state Markov process Based Maintenance Optimization fo...

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International Conference on Power System Technology (PowerCon)

作者： Liang Hai-feng Zhang Jing Gao Ya-jing North China Elect Power Univ State Key Lab Alternate Elect Power Syst Renewabl Baoding 071003 Peoples R China

ISBN: (纸本)9781479950324

Pointing at optimization of the distribution transformer's maintenance based on risk, firstly this paper proposed an equipment failure rate model based on Multi-state Markov process that is suitable for Condition Based Maintenance. And then an age reduction factor is used to modify the failure rate model. A Distribution Transformer Maintenance optimization Model whose objective function is the minimum of the risk of grid operation is established basically. The model is established from the perspective of maintenance accounting the maintenance risk and failure risk. And a hybrid optimization algorithm is proposed to solve the optimized problem. Finally using a case validates the effectiveness of this proposed method.

关键词： Maintenance optimization Markova Process Age reduction factor hybrid optimization algorithm

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